CN109029881B - Track bed state evaluation method based on track rigidity and ground penetrating radar detection - Google Patents
Track bed state evaluation method based on track rigidity and ground penetrating radar detection Download PDFInfo
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- 230000000149 penetrating effect Effects 0.000 title claims abstract description 60
- 238000001514 detection method Methods 0.000 title claims abstract description 53
- 238000011156 evaluation Methods 0.000 title claims abstract description 23
- 238000011068 loading method Methods 0.000 claims abstract description 27
- 238000012423 maintenance Methods 0.000 claims abstract description 15
- 238000012360 testing method Methods 0.000 claims description 24
- 201000010099 disease Diseases 0.000 claims description 9
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 9
- 230000009467 reduction Effects 0.000 claims description 9
- 238000000034 method Methods 0.000 claims description 8
- 238000012545 processing Methods 0.000 claims description 7
- 230000006870 function Effects 0.000 claims description 6
- 238000012937 correction Methods 0.000 claims description 3
- 239000006185 dispersion Substances 0.000 claims description 3
- 238000000605 extraction Methods 0.000 claims description 3
- 238000000513 principal component analysis Methods 0.000 claims description 3
- 238000012706 support-vector machine Methods 0.000 claims description 3
- 238000012549 training Methods 0.000 claims description 3
- 230000008439 repair process Effects 0.000 abstract description 6
- 238000004451 qualitative analysis Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000004140 cleaning Methods 0.000 description 2
- 238000010219 correlation analysis Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000009472 formulation Methods 0.000 description 1
- 238000011065 in-situ storage Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000013441 quality evaluation Methods 0.000 description 1
- 238000004445 quantitative analysis Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M5/00—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings
- G01M5/0041—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings by determining deflection or stress
- G01M5/005—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings by determining deflection or stress by means of external apparatus, e.g. test benches or portable test systems
- G01M5/0058—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings by determining deflection or stress by means of external apparatus, e.g. test benches or portable test systems of elongated objects, e.g. pipes, masts, towers or railways
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
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Abstract
A track bed state evaluation method based on track rigidity and ground penetrating radar detection comprises the following steps: firstly, mounting a ground penetrating radar system on a mobile loading vehicle, wherein the mobile loading vehicle is used for detecting the rigidity of a track; secondly, a mileage system of the track rigidity detection and ground penetrating radar detection system simultaneously adopts an encoder at the end of a mobile loading vehicle axle and simultaneously utilizes a GPS signal to ensure that the track bed state reflected by detection data is consistent in time and space, and simultaneously, track rigidity and ground penetrating radar detection are carried out; and (4) reasonably selecting influence factors of different detection parameters on the state of the track bed by combining different maintenance types, evaluating the state of the track bed and determining whether corresponding maintenance and repair are required.
Description
Technical Field
The invention belongs to a quality evaluation method in the technical field of railway engineering, and particularly relates to a track bed state evaluation method.
Background
According to patent retrieval and analysis at home and abroad, no continuous detection and evaluation method for the state of the ballast bed exists at present. At present, the detection of the state of the ballast bed mainly comprises the steps of excavating the ballast bed at a certain position at a fixed point, sampling and screening, and testing the density and the track rigidity of the ballast bed by utilizing an in-situ test. In recent years, continuous detection of roadbed and track bed by using ground penetrating radar is carried out at home and abroad, and the evaluation of the track bed dirtiness and the like by the ground penetrating radar is mainly qualitative and is different from the currently common track bed dirtiness rate definition, so that the maintenance and the repair of the track bed cannot be guided by the detection of the ground penetrating radar.
Patent document CN106758604A discloses a track line moving loading vehicle and a loading method, which solve the problem of continuously loading and testing track line parameters, but cannot make sure whether the change of the parameters is caused by track itself or roadbed damage;
similarly, patent document CN107527067A discloses an intelligent identification method for railway roadbed defects based on ground penetrating radar, but the identification result is limited by the identification method itself, and the maintenance and repair of the track bed cannot be guided.
Disclosure of Invention
The invention is based on the detection and evaluation technology of track rigidity and ground penetrating radar, and combines two tests of reflecting the state of the track bed, namely track rigidity and ground penetrating radar detection. The track rigidity comprises the rigidity of the track bed and the elasticity of the fasteners, the base plates and the like, and basically reflects the elasticity of the track bed under the condition that the types of the fasteners and the base plates are the same; the ground penetrating radar can carry out qualitative analysis on the dirt degree and the water content condition of the track bed and carry out quantitative analysis on the thickness of the track bed. By reasonably selecting the evaluation parameters and analyzing the correlation, and combining the advantages of the evaluation parameters and the correlation on the state of the track bed, the reasonable influence factors of different parameters on the state of the track bed are determined, so that the evaluation on the state of the track bed is comprehensive and accurate, and the maintenance of the track bed is guided.
The technical scheme adopted by the invention is as follows:
a track bed state evaluation method based on track rigidity and ground penetrating radar detection comprises the following steps: firstly, will visit the ground
The radar system is arranged on a mobile loading vehicle, and the mobile loading vehicle is used for detecting the rigidity of the track;
secondly, a mileage system of the track rigidity detection and ground penetrating radar detection system simultaneously adopts an encoder at the end of a mobile loading vehicle axle and simultaneously utilizes a GPS signal to ensure that the track bed state reflected by detection data is consistent in time and space, and simultaneously, track rigidity and ground penetrating radar detection are carried out;
and (4) reasonably selecting influence factors of different detection parameters on the state of the track bed by combining different maintenance types, evaluating the state of the track bed and determining whether corresponding maintenance and repair are required.
Further, different weights are given to track rigidity and ground penetrating radar test parameters, and evaluation indexes of the track bed state are obtained by utilizing a fuzzy algorithm.
Further, reasonable influence factors of different parameters on the track bed state are determined by reasonably selecting evaluation parameters and analyzing the correlation and combining the advantages of the evaluation parameters and the correlation on the track bed state.
Further, the correlation analysis is to select positions with different test parameters detected by a plurality of ground penetrating radars, apply different loads respectively, record track rigidity parameters with different loads under the same ground penetrating radar test parameters and track rigidity parameters with different ground penetrating radar test parameters under the same loads, and establish regression functions of the test parameters detected by the ground penetrating radars and the track rigidity parameters under different loads; recording the actual roadbed disease condition of the ballast bed, converting the attribute characteristic value of each instance data into a new sample, storing the new sample in a database, continuously accumulating, correcting the regression function and optimizing a learning model in the fuzzy algorithm.
Further, the mobile loading cart comprises: the device comprises a mobile loading device, a deformation amount acquisition device, a position parameter acquisition device and a data acquisition and processing device; the position parameter acquisition device comprises an encoder at the end of the mobile loading vehicle axle and a GPS signal device, when the mobile loading device moves on the track to be detected by an initial load or a detection load, the data acquisition and processing device acquires the position point parameters through the position parameter acquisition device and simultaneously acquires the track deformation value through the deformation amount acquisition device, and the track rigidity is obtained by calculating the deformation value detected during the initial load and the deformation value detected during the load detection corresponding to the same position point parameters.
Further, the ground penetrating radar detection specifically includes:
1) detecting normal railway subgrades, railway subgrades containing different types of subgrade diseases, railway bridges and turnouts by using a ground penetrating radar, and storing detection data;
2) pretreatment: carrying out zero line correction on the detection data, and converting the detection data into a gray image;
3) two-dimensional dispersion: dividing the obtained ground penetrating radar image into a plurality of identification units along the mileage direction, wherein each identification unit comprises 50-150 channels of data, and dividing each identification unit into a plurality of identification subunits along the depth direction, and two adjacent identification subunits have 50% overlapping areas;
4) feature extraction: extracting all characteristic values by taking the identification subunits as a unit, wherein the characteristic values of all the identification subunits of each identification unit form a characteristic vector of the identification unit, and the initial dimension M of the characteristic vector is equal to the number of the identification subunits multiplied by the number of the characteristic values;
5) and (3) feature dimensionality reduction: determining a dimensionality reduction N, and performing dimensionality reduction on the feature vector by utilizing principal component analysis to construct a low-dimensional feature vector;
6) constructing a recognition model: and establishing a support vector machine classifier, inputting the low-dimensional feature vector into the classifier, training the classifier, and establishing a railway roadbed disease intelligent identification model based on the ground penetrating radar.
The invention has the beneficial effects that:
on the basis that the same mileage system is adopted in the track rigidity and ground penetrating radar test, the GPS is further utilized for identification due to the frequency difference between the track rigidity and the ground penetrating radar test, the accumulated error between the track rigidity and the ground penetrating radar test is further corrected, the uniformity of the rigidity detection and the ground penetrating radar detection in time and space is ensured, and the analysis and the processing of subsequent data are facilitated. Different weights are given to the track rigidity and the ground penetrating radar test parameters, and the evaluation index of the track bed state is obtained by utilizing a fuzzy algorithm, so that the track rigidity and the ground penetrating radar test are organically and intelligently combined together, and can be continuously improved through a learning model. And the state of the track bed is evaluated by really combining the qualitative analysis of the ground penetrating radar and the quantitative numerical value of the track rigidity and combining the historical maintenance and repair data, and the establishment and implementation of a large-machine cleaning and repairing plan are guided.
Detailed Description
The present invention will be described in detail with reference to the following embodiments. It should be understood that the scope of the above-described subject matter is not limited to the following examples, and any techniques implemented based on the disclosure of the present invention are within the scope of the present invention.
A track bed state evaluation method based on track rigidity and ground penetrating radar detection comprises the following steps:
firstly, mounting a ground penetrating radar system on a mobile loading vehicle, wherein the mobile loading vehicle is used for detecting the rigidity of a track;
secondly, a mileage system of the track rigidity detection and ground penetrating radar detection system simultaneously adopts an encoder at the end of a mobile loading vehicle axle and simultaneously utilizes a GPS signal to ensure that the track bed state reflected by detection data is consistent in time and space, and simultaneously, track rigidity and ground penetrating radar detection are carried out;
and (4) reasonably selecting influence factors of different detection parameters on the state of the track bed by combining different maintenance types, evaluating the state of the track bed and determining whether corresponding maintenance and repair are required.
Further, different weights are given to track rigidity and ground penetrating radar test parameters, and evaluation indexes of the track bed state are obtained by utilizing a fuzzy algorithm.
Further, reasonable influence factors of different parameters on the track bed state are determined by reasonably selecting evaluation parameters and analyzing the correlation and combining the advantages of the evaluation parameters and the correlation on the track bed state.
Further, the correlation analysis is to select positions with different test parameters detected by a plurality of ground penetrating radars, apply different loads respectively, record track rigidity parameters with different loads under the same ground penetrating radar test parameters and track rigidity parameters with different ground penetrating radar test parameters under the same loads, and establish regression functions of the test parameters detected by the ground penetrating radars and the track rigidity parameters under different loads; recording the actual roadbed disease condition of the ballast bed, converting the attribute characteristic value of each instance data into a new sample, storing the new sample in a database, continuously accumulating, correcting the regression function and optimizing a learning model in the fuzzy algorithm.
Further, the mobile loading cart comprises: the device comprises a mobile loading device, a deformation amount acquisition device, a position parameter acquisition device and a data acquisition and processing device; the position parameter acquisition device comprises an encoder at the end of the mobile loading vehicle axle and a GPS signal device, when the mobile loading device moves on the track to be detected by an initial load or a detection load, the data acquisition and processing device acquires the position point parameters through the position parameter acquisition device and simultaneously acquires the track deformation value through the deformation amount acquisition device, and the track rigidity is obtained by calculating the deformation value detected during the initial load and the deformation value detected during the load detection corresponding to the same position point parameters.
Further, the ground penetrating radar detection specifically includes:
1) detecting normal railway subgrades, railway subgrades containing different types of subgrade diseases, railway bridges and turnouts by using a ground penetrating radar, and storing detection data;
2) pretreatment: carrying out zero line correction on the detection data, and converting the detection data into a gray image;
3) two-dimensional dispersion: dividing the obtained ground penetrating radar image into a plurality of identification units along the mileage direction, wherein each identification unit comprises 50-150 channels of data, and dividing each identification unit into a plurality of identification subunits along the depth direction, and two adjacent identification subunits have 50% overlapping areas;
4) feature extraction: extracting all characteristic values by taking the identification subunits as a unit, wherein the characteristic values of all the identification subunits of each identification unit form a characteristic vector of the identification unit, and the initial dimension M of the characteristic vector is equal to the number of the identification subunits multiplied by the number of the characteristic values;
5) and (3) feature dimensionality reduction: determining a dimensionality reduction N, and performing dimensionality reduction on the feature vector by utilizing principal component analysis to construct a low-dimensional feature vector;
6) constructing a recognition model: and establishing a support vector machine classifier, inputting the low-dimensional feature vector into the classifier, training the classifier, and establishing a railway roadbed disease intelligent identification model based on the ground penetrating radar.
And evaluating the state of the track bed by combining qualitative analysis of the ground penetrating radar and quantitative numerical values of track rigidity and combining maintenance and maintenance historical data, and guiding the formulation and implementation of a large-machine cleaning and maintenance plan.
The invention is not limited to the foregoing embodiments. The invention extends to any novel feature or any novel combination of features disclosed in this specification and any novel method or process steps or any novel combination of features disclosed.
Claims (3)
1. A track bed state evaluation method based on track rigidity and ground penetrating radar detection is characterized by comprising the following steps:
firstly, mounting a ground penetrating radar system on a mobile loading vehicle, wherein the mobile loading vehicle is used for detecting the rigidity of a track;
secondly, a mileage system of the track rigidity detection and ground penetrating radar detection system simultaneously adopts an encoder at the end of a mobile loading vehicle axle and simultaneously utilizes a GPS signal to ensure that the track bed state reflected by detection data is consistent in time and space, and simultaneously, track rigidity and ground penetrating radar detection are carried out;
reasonably selecting influence factors of different detection parameters on the state of the track bed by combining different maintenance types, evaluating the state of the track bed and determining whether corresponding maintenance is needed; giving different weights to track rigidity and ground penetrating radar test parameters, and obtaining an evaluation index of the track bed state by using a fuzzy algorithm; reasonable influence factors of different parameters on the state of the track bed are determined by reasonably selecting evaluation parameters and analyzing the correlation and combining the advantages of the evaluation parameters and the correlation on the state of the track bed; analyzing the correlation, namely selecting positions with different test parameters detected by a plurality of ground penetrating radars, respectively applying different loads, recording track rigidity parameters with different loads under the same ground penetrating radar test parameters and track rigidity parameters with different ground penetrating radar test parameters under the same loads, and establishing a regression function of the test parameters detected by the ground penetrating radars and the track rigidity parameters under different loads; recording the actual roadbed disease condition of the ballast bed, converting the attribute characteristic value of each instance data into a new sample, storing the new sample in a database, continuously accumulating, correcting the regression function and optimizing a learning model in the fuzzy algorithm.
2. The track bed state assessment method based on track stiffness and georadar detection as claimed in claim 1, wherein said mobile loading vehicle comprises: the device comprises a mobile loading device, a deformation amount acquisition device, a position parameter acquisition device and a data acquisition and processing device; the position parameter acquisition device comprises an encoder at the end of the mobile loading vehicle axle and a GPS signal device, when the mobile loading device moves on the track to be detected by an initial load or a detection load, the data acquisition and processing device acquires the position point parameters through the position parameter acquisition device and simultaneously acquires the track deformation value through the deformation amount acquisition device, and the track rigidity is calculated and acquired according to the deformation value detected when the initial load and the deformation value detected when the load is detected, which correspond to the same position point parameters.
3. The track bed state assessment method based on track stiffness and ground penetrating radar detection as claimed in claim 1, wherein the ground penetrating radar detection specifically comprises:
1) detecting normal railway subgrades, railway subgrades containing different types of subgrade diseases, railway bridges and turnouts by using a ground penetrating radar, and storing detection data;
2) pretreatment: carrying out zero line correction on the detection data, and converting the detection data into a gray image;
3) two-dimensional dispersion: dividing the obtained ground penetrating radar image into a plurality of identification units along the mileage direction, wherein each identification unit comprises 50-150 channels of data, and dividing each identification unit into a plurality of identification subunits along the depth direction, and two adjacent identification subunits have 50% overlapping areas;
4) feature extraction: extracting all characteristic values by taking the identification subunits as a unit, wherein the characteristic values of all the identification subunits of each identification unit form a characteristic vector of the identification unit, and the initial dimension M of the characteristic vector is equal to the number of the identification subunits multiplied by the number of the characteristic values;
5) and (3) feature dimensionality reduction: determining a dimensionality reduction N, and performing dimensionality reduction on the feature vector by utilizing principal component analysis to construct a low-dimensional feature vector;
6) constructing a recognition model: and establishing a support vector machine classifier, inputting the low-dimensional feature vector into the classifier, training the classifier, and establishing a railway roadbed disease intelligent identification model based on the ground penetrating radar.
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| CN109948690A (en) * | 2019-03-14 | 2019-06-28 | 西南交通大学 | A kind of high-speed rail scene perception method based on deep learning and structural information |
| CN112044723B (en) * | 2020-07-13 | 2022-04-12 | 中南大学 | Method for evaluating state of railway dirty track bed |
| CN115791456B (en) * | 2023-02-06 | 2023-04-25 | 中国铁道科学研究院集团有限公司铁道建筑研究所 | Railway ballasted track rigidity evaluation method |
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| CN103207097B (en) * | 2013-04-03 | 2016-08-03 | 中国铁道科学研究院铁道建筑研究所 | Orbit rigidity dynamic testing method |
| CN103552579B (en) * | 2013-11-18 | 2016-01-20 | 中国神华能源股份有限公司 | Comprehensive detection train for freight heavy haul railway |
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| CN107527067B (en) * | 2017-08-01 | 2021-06-29 | 中国铁道科学研究院集团有限公司铁道建筑研究所 | Railway roadbed disease intelligent identification method based on ground penetrating radar |
| CN108009742B (en) * | 2017-12-15 | 2020-09-25 | 北京交通大学 | Method and system for determining health state of railway track |
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| 雷达无损检测新技术在铁路行业中的应用;王令朝;《铁道技术监督》;20060930;第34卷(第9期);第23-25页 * |
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